A novel iterative algorithm is proposed for sparse-view cone beam computed tomography (CBCT) reconstruction based on the weighted Schatten p-norm minimization (WSNM). By using the half quadratic splitting, the sparse-view CBCT reconstruction task is decomposed into two sub-problems that can be solved through alternating iteration: simple reconstruction and image denoising. The WSNM that fits well with the low-rank hypothesis of CBCT data is introduced to improve the denoising sub-problem as a regularization term. The experimental results based on the digital brain phantom and clinical CT data indicated the advantages of the proposed algorithm in both structural information preservation and artifacts suppression, which performs better than the classical algorithms in quantitative and qualitative evaluations.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1364/OE.404471 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Robot-assisted Endodontic Retreatment: A Case Report with Clinical Considerations.
J Endod
January 2025
State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan, China; Department of Cariology and Endodontics, School and Hospital of Stomatology, Wuhan University, Wuhan, China. Electronic address:
Introduction: Fiber posts present significant challenges for nonsurgical endodontic retreatment, as improper removal may result in iatrogenic root perforation or even root fracture. Recently, robotic technology has attracted considerable attention in dentistry and active dental robotic (ADR) systems can perform procedures based on preset instructions, minimizing reliance on the dentist's experience. This case report describes the application of an ADR system for fiber post removal through an existing zirconia crown.
View Article and Find Full Text PDFModel-based perfusion reconstruction with time separation technique in cone-beam CT dynamic liver perfusion imaging.
Med Phys
January 2025
Institute for Medical Engineering, Otto von Guericke University Magdeburg, Magdeburg, Germany.
Background: The success of embolization, a minimally invasive treatment of liver cancer, could be evaluated in the operational room with cone-beam CT by acquiring a dynamic perfusion scan to inspect the contrast agent flow.
Purpose: The reconstruction algorithm must address the issues of low temporal sampling and higher noise levels inherent in cone-beam CT systems, compared to conventional CT.
Methods: Therefore, a model-based perfusion reconstruction based on the time separation technique (TST) was applied.
A dual-domain network with division residual connection and feature fusion for CBCT scatter correction.
Phys Med Biol
January 2025
School of Biomedical Engineering, ShanghaiTech University, No. 1 Zhongke Road, Pudong New Area, Shanghai, Shanghai, 201210, CHINA.
Objective: This study aims to propose a dual-domain network that not only reduces scatter artifacts but also retains structure details in CBCT.
Approach: The proposed network comprises a projection-domain sub-network and an image-domain sub-network. The projection-domain sub-network utilizes a division residual network to amplify the difference between scatter signals and imaging signals, facilitating the learning of scatter signals.
In image-guided radiotherapy (IGRT), four-dimensional cone-beam computed tomography (4D-CBCT) is critical for assessing tumor motion during a patients breathing cycle prior to beam delivery. However, generating 4D-CBCT images with sufficient quality requires significantly more projection images than a standard 3D-CBCT scan, leading to extended scanning times and increased imaging dose to the patient. To address these limitations, there is a strong demand for methods capable of reconstructing high-quality 4D-CBCT images from a 1-minute 3D-CBCT acquisition.
View Article and Find Full Text PDFA platform combining automatic segmentation and automatic measurement of the maxillary sinus and adjacent structures.
Clin Oral Investig
January 2025
State Key Laboratory of Oral Diseases & National Center for Stomatology & National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, 610041, China.
Objectives: To develop a platform including a deep convolutional neural network (DCNN) for automatic segmentation of the maxillary sinus (MS) and adjacent structures, and automatic algorithms for measuring 3-dimensional (3D) clinical parameters.
Materials And Methods: 175 CBCTs containing 242 MS were used as the training, validating and testing datasets at the ratio of 7:1:2. The datasets contained healthy MS and MS with mild (2-4 mm), moderate (4-10 mm) and severe (10- mm) mucosal thickening.
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!